Application of machine learning for online reputation systems
Authors : Fadi Almasalha, Ahmad Alqwadri, Mohammad Azzeh
Abstract : Users on the Internet usually require venues to provide better purchasing recommendations. This can be provided by a reputation system that processes ratings to provide recommendations. The rating aggregation process is a main part of reputation systems to produce global opinions about the product quality. Naive methods that are frequently used do not consider consumer profiles in their calculations and cannot discover unfair ratings and trends emerging in new ratings. Other sophisticated rating aggregation methods that use a weighted average technique focus on one or a few aspects of consumers’ profile data. This paper proposes a new reputation system using machine learning to predict reliability of consumers from their profile. In particular, we construct a new consumer profile dataset by extracting a set of factors that have a great impact on consumer reliability, which serve as an input to machine learning algorithms. The predicted weight is then integrated with a weighted average method to compute product reputation score. The proposed model has been evaluated over three MovieLens benchmarking datasets, using 10-folds cross validation. Furthermore, the performance of the proposed model has been compared to previous published rating aggregation models. The obtained results were promising which suggest that the proposed approach could be a potential solution for reputation systems. The results of the comparison demonstrated the accuracy of our models. Finally, the proposed approach can be integrated with online recommendation systems to provide better purchasing recommendations and facilitate user experience on online shopping markets.
Keywords : Reputation system, rating aggregation, machine learning, consumer reliability, user trust
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Investigation of applying machine learning for watch-list filtering in anti-money laundering
Authors : Fadi Almasalha, Mohannad Alkhalili, Mahmoud Qutqut
Abstract : Financial institutions must meet international regulations to ensure not to provide services to criminals and terrorists. They also need to continuously monitor financial transactions to detect suspicious activities. Businesses have many operations that monitor and validate their customer's information against sources that either confirm their identities or disprove. Failing to detect unclean transaction(s) will result in harmful consequences on the financial institution responsible for that such as warnings or fines depending on the transaction severity level. The financial institutions use Anti-money laundering (AML) software sanctions screening and Watch-list filtering to monitor every transaction within the financial network to verify that none of the transactions can be used to do business with forbidden people. Lately, the financial industry and academia have agreed that machine learning (ML) may have a significant impact on monitoring money transaction tools to fight money laundering. Several research work and implementations have been done on Know Your Customer (KYC) systems, but there is no work on the watch-list filtering systems because of the compliance risk. Thus, we propose an innovative model to automate the process of checking blocked transactions in the watch-list filtering systems. To the best of our knowledge, this paper is the first research work on automating the watch-list filtering systems. We develop a Machine Learning - Component (ML-Component) that will be integrated with the current watch-list filtering systems. Our proposed ML-Component consists of three phases; monitoring, advising, and take action. Our model will handle a known critical issue, which is the false-positives (i.e., transactions that are blocked by a false alarm). Also, it will minimize the compliance officers' effort, and provide faster processing time. We performed several experiments using different ML algorithms (SVM, DT, and NB) and found that the SVM outperforms other algorithms. Because our dataset is nonlinear, we used the polynomial kernel and achieved higher accuracy for predicting the transactionś decision, and the correlation matrix to show the relationship between the numeric features.
Keywords : Anti-money laundering, financial transactions monitoring, machine learning (ML), sanctions screening, watch-list filtering
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The Role of Data Pre-processing Techniques in Improving Machine Learning Accuracy for Predicting Coronary Heart Disease
Authors : Fadi Almasalha, Osamah Sami, Yousef Elsheikh
Abstract : "These days, in light of the rapid developments, people work day and night to live at a good level. This often causes them to not pay much attention to a healthy lifestyle, such as what they eat or even what physical activities they do. These people are often the most likely to suffer from coronary heart disease. The heart is a small organ responsible for pumping oxygen-rich blood to the rest of the human body through the coronary arteries. Accordingly, any blockage or narrowing in one of these coronary arteries may cause blood not to be pumped to the heart and from it to the rest of the body, and thus cause what is known as heart attacks. From here, the importance of early prediction of coronary heart disease has emerged, as it can help these people change their lifestyle and eating habits to become healthier and thus prevent coronary heart disease and avoid death. This paper improve the accuracy of machine learning techniques in predicting coronary heart disease using data preprocessing techniques. Data preprocessing is a technique used to improve the efficiency of a machine learning model by improving the quality of the feature. The popular Framingham Heart Study dataset was used for validation purposes. The results of the research paper indicate that the use of data preprocessing techniques had a role in improving the predictive accuracy of poorly efficient classifiers, and shows satisfactory performance in determining the risk of coronary heart disease. For example, the Decision Tree classifier led to a predictive accuracy of coronary heart disease of 91.39% with an increase of 1.39% over the previous work, the Random Forest classifier led to a predictive accuracy of 92.80% with an increase of 2.7% over the previous work, the K-Nearest Neighbor classifier led to a predictive accuracy of 92.68% with an increase of 2.58% over the previous work, the Multilayer Perceptron Neural Network (MLP) classifier led to a predictive accuracy of 92.64% with an increase of 2.64% over the previous work, and the Na¨ıve Bayes classifier led to a predictive accuracy of 90.56% with an increase of 0.66% over the previous work. "
Keywords : Coronary heart disease, heart, machine learning, data preprocessing, classification technique
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Overview of the Current Status of NoSQL Database
Authors : Fadi Almasalha, Yasmin Rasheed, Mahmoud Qutqut
Abstract : "Nowadays with the accelerated development of the Internet and Cloud computing, the fast growth of technology generates a massive amount of data. Businesses and people generate these data by using web apps, social media, and new technologies. These data, in general, could be structured, semi-structured or unstructured. Because of the different types of data and the big data that is generated, there is a need for a database to be able to store and process these data effectively to enhance the performance when reading and writing. So, there is a need for a new design for the database, and it is not suitable for storing, analyzing and performing data in a relational database for big data. In addition to that, many new challenges faced the traditional relational database; especially in the applications that required large scale and high concurrency such as search engines. In response to that, NoSQL has developed to solve these types of problem. NoSQL database has many advantages that make it gain significant popularity over the last few years and used widely. It read and write the data quickly, expands easily, low cost and many other features. In this paper, we overview the NoSQL database and its characteristics in the field of the Internet of Things (IoT). We also provide two representative use cases of using the NoSQL database in current technologies "
Keywords : NoSQL; database; relational database; IoT
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Hybrid user action prediction system for automated home using association rules and ontology
Authors : Fadi Almasalha, Amneh Shaban, Mahmoud Qutqut
Abstract : Nowadays, with the rapid increase of Internet users, the Internet services dominate a primary part of our lifestyle. Moreover, the evolution of the internet of things has introduced new insights into smart platforms and devices that leads to the new vision of ‘smart homes’. The idea of smart homes is not a recent concept; it has been in high interest by both academia and industry to make smart homes a more convenient technology for human's comfort. In this study, the authors propose a new hybrid prediction system based on the frequent pattern (FP)-growth and ontology graphs for home automation systems. Their proposed system simulates the human prediction actions by adding common sense data by utilizing the advantages of the ontology graph and the FP-growth to find a better solution in predicting home user actions for automated systems. For the evaluation of the proposed system, two ontology graphs are introduced with FP-growth to achieve the best results. Both graphs are tested through multiple weight values with the results of FP-growth. As a result, the best weight distribution selected in this study is (70, 30) for time and location ontology graphs respectively. Their results showed that the proposed prediction system achieved an accuracy of 79% for all weekdays and 81% excluding weekend days.
Keywords : Internet; home automation; ontologies (artificial intelligence); data mining
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Comprehensive survey of the IoT open-source OSs
Authors : Fadi Almasalha, Aya Al-Sakran, Mahmoud Qutqut, Hossam S. Hassanein
Abstract : The Internet of things (IoT) has attracted a great deal of research and industry attention recently and is envisaged to support diverse emerging domains including smart cities, health informatics, and smart sensory platforms. Operating system (OS) support for IoT plays a pivotal role in developing scalable and interoperable applications that are reliable and efficient. IoT is implemented by both high-end and low-end devices that require OSs. Recently, the authors have witnessed a diversity of OSs emerging into the IoT environment to facilitate IoT deployments and developments. In this study, they present a comprehensive overview of the common and existing open-source OSs for IoT. Each OS is described in detail based on a set of designing and developmental aspects that they established. These aspects include architecture and kernel, programming model, scheduling, memory management, networking protocols support, simulator support, security, power consumption, and support for multimedia. They present a taxonomy of the current IoT open-source OSs. The objective of this survey is to provide a well-structured guide to developers and researchers to determine the most appropriate OS for each specific IoT devices/applications based on their functional and non-functional requirements. They remark that this is the first such tutorial style paper on IoT OSs.
Keywords : operating systems(computers), Internet of Things, public domain software
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Software testing techniques in iot
Authors : Fadi Almasalha, Abdallah Qusef, Aalaa Badarneh, Ghadeer Murad
Abstract : As well as the important industry 4.0 revolution, based on of the serves that IoT provides in our society in different fields such as smart building, factory, mobility, health care. Previous studies explore various technology solution to find the best technique to test IoT applications in order to ensure quality for IoT devices. The paper surveys diverse of aspects of multiples software testing and tools for loT devices, and provides details in use case testing for IoT environment and test different accepts such as usability, security, connectivity. The paper considers various security requirement and challenges an unveils different research problems in testing IoT applications and proposes multiple tools and software techniques that help to enhance IoT applications quality.
Keywords : Software Testing, IoT, Testing Methodologies, Testing Tools
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An overview of the Internet of things closed source operating systems
Authors : Fadi Almasalha, Aya Al-Sakran, Mahmoud Qutqut, Hossam S. Hassanein, Mohammad Hijjawi
Abstract : The Internet of Things (IoT) attract a great deal of research and industry attention recently and are envisaged to support diverse emerging domains including intelligent transportation, smart cities, and health informatics. Operating system support for IoT plays a pivotal role in developing interoperable and scalable applications that are efficient and reliable. IoT is implemented by both high-end and low-end devices that require an operating system to run. Recently, we have witnessed a diversity of OSs emerging into IoT environment to facilitate IoT deployments and developments. In this paper, we present an overview of the common and existing closed source OSs for IoT. This paper is written in a tutorial style where each OS is described in details based on a set of designing and development aspects that we established. These aspects include architecture and kernel, memory management, scheduling, power consumption, networking protocols support, security, programming model, and multimedia support. The objective of this survey is to provide a well-structured guide to developers and researchers to determine the most appropriate OS for each specific IoT applications/devices based on their functional and non-functional requirements
Keywords : Internet of Things (IoTs), Operating System (OS), closed source, high-end, low-end
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Secure transmission of multimedia contents over low-power mobile devices
Authors : Fadi Almasalha, Farid Naït-Abdesselam, Goce Trajcevski, Ashfaq Khokhar
Abstract : Secure transmission of multimedia contents is computationally challenging due to the sheer volume of data involved, and achieving this by fully encrypting multimedia contents is not a viable solution for mobile devices, as they run on limited battery power and employ relatively slower processors compared to their desktop counterparts. Also, in some particular application scenarios, the value, secrecy, and/or privacy of the information is time dependent and short lived. This paper presents a highly scalable approach for securing multimedia streams on mobile devices by encrypting intelligently only selected portions of the bit stream. More importantly, the proposed solution works mainly on compressed bit stream and does not require any media decoding. It encrypts on the average less than 3% of a packet load and provides robust security equivalent to that of a fully encrypted bit stream. This solution has been implemented on desktop, laptop, netbook and Nokia N series platforms. The proposed scheme is approximately 15 times faster (on average) and processes 8 to 12 times more information for a given battery life when compared to a state of the art full encryption based solutions.
Keywords : SecurityMultimedia communicationsLow-powerMobile devices
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A New Steganography Technique using JPEG Images
Authors : Fadi Almasalha, Rand A. Watheq, Mahmoud Qutqut
Abstract : "Steganography is a form of security technique that using ambiguity to hide a secret message within an ordinary message between senders and receivers. In this paper, we propose a new steganography technique for hiding data in Joint Photographic Experts Group (JPEG) images as it is the most known type of image compression between the lossy type compressions. Our proposed work is based on lossy compression (frequency domain) in images. This type of compression is susceptible to change even for the smallest amount of change which raises a difficulty to find a proper location to embed data. This should be done without affecting the image quality and without allowing anyone to notice the hidden message. From the senders side, first, we divide the image into 8*8 blocks, then apply a Discrete Cosine Transform (DCT), Quantization, and zigzag processes respectively. Second, the secret message is embedded at the end of each selected zigzag block array using the best method of our experimental results. Third, the rest of the code applies the Run Length Code (RLC), Different Pulse Code Modularity (DPCM) and Huffman encoder to obtain the compressed image that includes the embedded message. From the receiver’s side, we will reverse the previous steps to extract the secret message using an encrypted shared key via a secure channel. Our experimental results show that the best array content size of zigzag computed coefficients are between 1 to 20. This selection allows us to utilize more than half of the image blocks to embed the secret message and the difference between the cover image that holds the secret message and the original cover image is very minimal and hard to detect. "
Keywords : Steganography, hide secret message, JPEG image, lossy compression, frequency domain, zigzag
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Video security in Internet of things: an overview
Authors : Fadi Almasalha, Mina Alcharchafchi, Mahmoud Qutqut
Abstract : "Internet of Things (IoT) became one of the core networking paradigms nowadays. IoT is already interconnected millions of things (e.g., sensors, appliances, video cameras, devices); and expected soon to connect billions of new devices. An important challenge for supporting multimedia applications in the IoT is the security heterogeneity of several of technologies, devices, and protocols. In this paper, we overview the video streaming in the IoT networks; focusing on the security of video in IoT. We present a comprehensive overview of security issues and challenges in video streaming in IoT in order for a better understanding. "
Keywords : Internet of Things, Security, Video Streaming, Video Traffic, IoT Video, Video Security.
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Pareto efficient multi-objective optimization for local tuning of analogy-based estimation
Authors : Fadi Almasalha, Mohammad Azzeh, Ali Bou Nassif, Shadi Banitaan
Abstract : Analogy-based effort estimation (ABE) is one of the prominent methods for software effort estimation. The fundamental concept of ABE is closer to the mentality of expert estimation but with an automated procedure in which the final estimate is generated by reusing similar historical projects. The main key issue when using ABE is how to adapt the effort of the retrieved nearest neighbors. The adaptation process is an essential part of ABE to generate more successful accurate estimation based on tuning the selected raw solutions, using some adaptation strategy. In this study, we show that there are three interrelated decision variables that have great impact on the success of adaptation method: (1) number of nearest analogies (k), (2) optimum feature set needed for adaptation and (3) adaptation weights. To find the right decision regarding these variables, one need to study all possible combinations and evaluate them individually to select the one that can improve all prediction evaluation measures. The existing evaluation measures usually behave differently, presenting sometimes opposite trends in evaluating prediction methods. This means that changing one decision variable could improve one evaluation measure while it is decreasing the others. Therefore, the main theme of this research is how to come up with best decision variables that improve adaptation strategy and thus the overall evaluation measures without degrading the others. The impact of these decisions together has not been investigated before; therefore, we propose to view the building of adaptation procedure as a multi-objective optimization problem. The Particle swarm optimization algorithm (PSO) is utilized to find the optimum solutions for such decision variables based on optimizing multiple evaluation measures. We evaluated the proposed approaches over 15 datasets and using four evaluation measures. After extensive experimentation, we found that: (1) predictive performance of ABE has noticeably been improved, (2) optimizing all decision variables together is more efficient than ignoring any one of them, and (3) optimizing decision variables for each project individually yields better accuracy than optimizing them for the whole dataset.
Keywords : Analogy-based effort estimation, Adaptation strategy, Particle swarm optimization, Multi-objective optimization
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Partial encryption of entropy-coded video compression using coupled chaotic maps
Authors : Fadi Almasalha, Rogelio Hasimoto Beltran, Ashfaq A. Khokhar
Abstract : Due to pervasive communication infrastructures, a plethora of enabling technologies is being developed over mobile and wired networks. Among these, video streaming services over IP are the most challenging in terms of quality, real-time requirements and security. In this paper, we propose a novel scheme to efficiently secure variable length coded (VLC) multimedia bit streams, such as H.264. It is based on code word error diffusion and variable size segment shuffling. The codeword diffusion and the shuffling mechanisms are based on random operations from a secure and computationally efficient chaos-based pseudo-random number generator. The proposed scheme is ubiquitous to the end users and can be deployed at any node in the network. It provides different levels of security, with encrypted data volume fluctuating between 5.5–17%. It works on the compressed bit stream without requiring any decoding. It provides excellent encryption speeds on different platforms, including mobile devices. It is 200% faster and 150% more power efficient when compared with AES software-based full encryption schemes. Regarding security, the scheme is robust to well-known attacks in the literature, such as brute force and known/chosen plain text attacks
Keywords : chaotic maps; Real-Time Protocol (RTP); video encoding; Huffman code
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A students attendance system using QR code
Authors : Fadi Almasalha, Nael Hirzallah
Abstract : "Smartphones are becoming more preferred companions to users than desktops or notebooks. Knowing that smartphones are most popular with users at the age around 26, using smartphones to speed up the process of taking attendance by university instructors would save lecturing time and hence enhance the educational process. This paper proposes a system that is based on a QR code, which is being displayed for students during or at the beginning of each lecture. The students will need to scan the code in order to confirm their attendance. The paper explains the high level implementation details of the proposed system. It also discusses how the system verifies student identity to eliminate false registrations. "
Keywords : Mobile Computing, Attendance System, Educational System, GPS
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Current state of pain care for hospitalized patients at end of life
Authors : Fadi Almasalha, Yingwei yao, Gail, Karen Dunn Lopez, Ashfaq Khokar, Andrew Johnson, Rashid Ansari, Diana J. Wilkie
Abstract : We report findings on the current state of pain care in hospitals for end-of-life (EOL) patients using longitudinal data from 8 diverse medical–surgical units located in 4 different Midwestern hospitals over 24 months. We identified 1425 EOL care episodes, 596 (41.3%) of which had a pain diagnosis. The percentage of EOL patients with pain varied significantly across units (P < .001) and was even lower (27.7%) for those with “acute confusion.” Additionally, 30% of EOL patients had severe or significant pain at death or discharge to hospice and only 42.7% actually met the expected pain-related outcome ratings. Pain often improved within 48 hours of admission (P < .005), the improvement, however, stagnated following this initial time period (P = .92). A sizable gap between pain science and clinical practice continues.
Keywords : pain outcomes, electronic health records, standardized terminology, end of life
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Data mining nursing care plans of end‐of‐life patients: A study to improve healthcare decision making
Authors : Fadi Almasalha, Ashfaq A. Khokhar, Dianhui Xu, Gail M Keenan, Yingwei Yao, Chaing Chen Yu, Andrew Edward Johnson, Rashid Ansari, Diana J. Wilkie
Abstract : PURPOSE:To reveal hidden patterns and knowledge present in nursing careinformation documented with standardized nursing terminologies on end-of-life(EOL) hospitalized patients.METHOD:596 episodes of care that included pain as a problem on a patient’scare plan were examined using statistical and data mining tools. The data wereextracted from the Hands-On Automated Nursing Data System database ofnursing care plan episodes (n=40,747) coded with NANDA-I, Nursing OutcomesClassification, and Nursing Intervention Classification (NNN) terminologies.System episode data (episode=care plans updated at every hand-off on a patientwhile staying on a hospital unit) had been previously gathered in eight unitslocated in four different healthcare facilities (total episodes=40,747; EOL epi-sodes=1,425) over 2 years and anonymized prior to this analyses.RESULTS:Results show multiple discoveries, including EOL patients with hospi-tal stays (<72 hr) are less likely (p<.005) to meet the pain relief goals comparedwith EOL patients with longer hospital stays.CONCLUSIONS:The study demonstrates some major benefits of systematicallyintegrating NNN into electronic health records.The electronic health record (EHR) became a priority ofthe U.S. federal government as a result of the Health Infor-mation Technology for Economic and Clinical Health(HITECH) Act of 2009. Through the Act, health providerswill be given incentives to adopt and use EHRs with allpatients by 2015 (Managed Care Outlook, 2010). The aim ofHITECH is to improve the quality of patient care by enhanc-ing the efficiency of healthcare delivery systems. With theanticipated increase in use of EHRs, the delivery systemsare likely to amass and archive an unprecedented amountof health-related information in a relatively short period oftime. However, the mere deployment of an EHR will haveonly limited benefits unless novel applications are devel-oped that extract information nuggets hidden in the cap-tured data, and use it as feedback or a knowledge base tocontinuously improve the quality of health care. This fact isborne out in numerous studies in which investigators havereported major gaps in the availability and usefulness ofinformation in the EHRs and patient record systems to thefrontline users (Allen, 1998; Hardey, Payne, & Coleman,2000; Karkkainen, Bondas, & Eriksson, 2005; Keenan &Yakel, 2005; Keenan,Yakel, Dunn Lopez, Tschannen, & Ford,bs_bs_banner15© 2012, The AuthorsInternational Journal of Nursing Knowledge © 2012, NANDA InternationalInternational Journal of Nursing Knowledge Volume 24, No. 1, February 2013
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Performance evaluation of chaotic and conventional encryption on portable and mobile platforms
Authors : Rogelio Hasimoto-Beltran, Fadi Al-Masalha & Ashfaq Khokhar
Abstract : Protection of private user information in computers and communication networks has been one of the major concerns during the last decade. It has become even more critical due to pervasive use of smart mobile devices, and is exacerbated due to their limited processing and battery power needed to manage complex encryption schemes, particularly for real-time multimedia applications (audio and video). Secure multimedia communication systems require processing of huge amounts of information at speeds ranging from Kilobits/sec (Kbs) to the order of Megabits/sec (Mbs). Provisioning of security for such large volumes of data in mobile devices may be simply infeasible when the complexity of related operations is beyond the processing limit of such devices. In this chapter we evaluate the performance of different encryption schemes, including AES implementations and non-conventional chaotic encryption on different architectures. Our experiments reveal that chaos-based schemes outperform the conventional AES implementation in terms of CPU usage, encryption speed, and energy consumption. Particularly they consume 300-400% less CPU power, and have over 250% faster encryption speed. However, the performance also depends on the floating point capability of the platform; a suitable scheme may be chosen depending on the CPU power of platform. The performance results reported in this chapter are based on experiments on contemporary desktops, laptops, netbooks, and cell phones (Nokia N800 and N900).
Keywords : Encryption Scheme, Advance Encryption Standard, Unstable Periodic Orbit, Image Encryption Scheme, Brute Force Attack
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Scalable encryption of variable length coded video bit streams
Authors : Fadi Almasalha, Rogelio Hasimoto Beltran, Ashfaq A. Khokhar
Abstract : This paper presents a novel scheme to efficiently secure variable length coded (VLC) multimedia bit streams, such as H.264. The proposed scheme employs code word diffusion and content based shuffling techniques to achieve security. Specifically, it is a combination of a highly secure random number generator based on chaotic maps and a low computation complexity block shuffling procedures. The scheme is ubiquitous to the end users and can be deployed at any node in the network, without requiring any decoding. Its power efficiency and scalability is demonstrated on mobile as well as fixed platforms.
Keywords : Multimedia, RTP, Chaotic Maps, Encryption
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Selective encryption based data security for Ogg streams
Authors : Fadi Almasalha, Ashfaq Khokhar, Shehab Baqai
Abstract : Existing solutions for securing multimedia streams such as RTP and Ogg, particularly those based on encrypting large portions of the data stream, are highly unscalable, and infringe on the Quality of Service (QoS) requirements as well. In this paper, we propose an efficient selective encryption technique for securing Ogg formatted VoIP/video streams. The proposed solution encrypts only 1.5% of the stream while guaranteeing security/privacy of the entire bit stream. Experimental results based on example hardware platforms while simulating different digital attacks are presented to verify the robustness of the proposed method.
Keywords : Ogg, VoIP, Selective Encryption
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Scalable security of streaming multimedia contents
Authors : Fadi Almasalha, Ashfaq Khokhar, Shehab Baqai, Afshan Amin
Abstract : Due to pervasive communication infrastructures a plethora of enabling technologies is being developed over mobile and wired networks. Among these, Voice over IP (VOIP) and video telephony are the most challenging services in terms of quality, real-time requirements, and security. In this paper we investigate data security issues related to streaming of G.729 speech codec bitstreams. We propose a novel scheme that selectively encrypts these contents while providing robust security and guarantee privacy of the contents. The proposed scheme is based on selective encryption and content based shuffling of 60% of each frame contents. The shuffling is achieved using secure hash maps. The proposed scheme is ubiquitous to the end users and can be deployed at any node in the network.
Keywords : component, G72, Selective Encryption, VoIP, AES, RTP, PESQ, MOS
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Secure multimedia transmission over RTP
Authors : Fadi Almasalha, Ashfaq Khokhar, Nikita Agarwal
Abstract : The evolution of wireless handsets from simple mobile phones to smart devices, capable of capturing multimedia information and accessing Internet, has enabled its users to share multimedia contents with other network-capable devices in the vicinity or across the Internet. However, multimedia contents may include private information that a user may share only if it is secure from sniffing and other digital attacks.Achieving security by fully encrypting multimedia contents is not a viable solution for mobile devices, as they run on limited battery power and employ relatively slower processors compared to their desktop counterparts. These limitations have forced the handsets manufacturers to move the security operations to other parts of the networks thus compromising scalability as well as privacy. This paper presents a highly scalable approach for securing multimedia streams on mobile devices by encrypting intelligently selected portions of the coded bit stream.The proposed solution does not require media decoding and encrypts on the average less than 1% of the packet load. For all practical purposes, the proposed scheme provides robust security equivalent to that of a fully encrypted bit stream. The proposed scheme has been implemented on Nokia devices running Maemo, a mobile version of Debian distribution.
Keywords : RTP, Secure Transmission, Selective ecnryption
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